Analytics Theory and applications for teaching children with AUTISM spectrum disorder
Self-funded PhD students only
School of Computing
February and October
Applications accepted all year round
Applications are invited for a self-funded, 3-year full-time or 6-year part-time PhD project, to commence in October 2019 or February 2020.
Machine enhanced therapy/intervention for children with ASD has been propelled by the innovations in human-machine interfaces and computer vision. Impressive results in the sensing and analytics of ASD children’s behaviour have enabled the avenue for more dexterous interaction taking into account their less preference for interacting with non-human agents. Despite the significant attention in machine assisted healthcare for children with ASD, an educational purpose targeted machine assisted system is still missing. The knowledge and valuable data from machine enhanced therapy/intervention has not been converted into actionable application in special education yet.
The goal of this PhD project is to develop a better understanding of how machine assisted education systems are more effective at a reduced burden of human intervention and build a machine assisted education system for children with ASD. To achieve this aim, the state-of-the-art human behaviour sensing and analytics techniques will be transformed into a real application with an emphasis on the curriculum design, affective computing and system integration. The outcome of this PhD project will enable the special education school users to reduce their repeated workload in daily teaching while observing the progress of children behaviour/knowledge with quantitative measurements.The project will involve building a virtual environment based curriculum and knowledge visualisation in the special education domain of ASD, developing an affective computing framework for children behaviour analysis comprising gaze estimation, expression recognition and motion recognition, and the contactless sensory system integration for an education targeted platform. Based on the tangible system, a long-term evaluation of the machine assisted education for children with ASD will be conducted in special education schools. Experiments will be run to assess how effectively the burden of teachers is reduced and how ASD children benefit from the machine assisted teaching of knowledge and skills.
PhD full-time and part-time courses are eligible for the Government Doctoral Loan (UK and EU students only).
2019/2020 entryHome/EU/CI full-time students: £4,327 p/a*
Home/EU/CI part-time students: £2,164 p/a*
International full-time students: £15,900 p/a*
International part-time students: £7,950 p/a*
*Fees are subject to annual increase
By Publication Fees 2019/2020Members of staff: £1,610 p/a*
External candidates: £4,327 p/a*
*Fees are subject to annual increase
You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in an Civil Engineering or related area. In exceptional cases, we may consider equivalent professional experience and/or Qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
The candidate should have a UK Honours Degree at 2.1 (or equivalent) in Computing Science or related area. A good understanding of OpenCV and related programming skills are ideally preferred for shortlisting the candidates.
How to apply
We’d encourage you to contact Dr Honghai Liu (email@example.com) to discuss your interest before you apply, quoting the project code CCTS4540219.
When you're ready to apply, you can use our online application form and select ‘Computing and Creative Technologies’ as the subject area. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV.
Our How to Apply page also offers further guidance on the PhD application process.